USE OF WIM IN SOUTHERN AFRICA Current / Future Louw Kannemeyer 2 - PowerPoint PPT Presentation

1 USE OF WIM IN SOUTHERN AFRICA Current / Future Louw Kannemeyer

2 Contents • Road Network • Current WIM Use • Future WIM Use

SA ROAD NETWORK - 2018 Authority Paved Gravel Total SANRAL 22 214 0 22 214 Provinces - 9 46 548 226 273 272 821 Metros - 8 51 682 14 461 66 143 Municipalities 37 680 219 223 256 903 Total 158 124 459 957 618 081 Un-Proclaimed (Estimate) 131 919 131 919 Estimated Total 158 124 591 876 750 000 Un-Proclaimed Roads = Public roads not formally gazetted by any Authority

Road Rank Country length (km) World 64 285 009 1 United States 6 586 610 2 4 689 842 India South Africa has the 10 th 3 4 237 500 China longest total and 18 th longest 4 1 751 868 Brazil 5 1 210 251 Japan paved road network in the 6 1 042 300 Canada 7 982 000 Russia world 8 France 951 200 9 823 217 Australia 10 750 000 South Africa The National Development 11 681 298 Spain 12 Germany 644 480 Plan states that roads 13 572 900 Sweden represent one of the largest 14 487 700 Italy 15 437 759 Indonesia public infrastructure 16 426 906 Turkey investments in most countries. … … … 34 153 497 Dem Rep of Congo 45 Zimbabwe 97 267 54 91 440 Zambia 55 91 049 Tanzania RSA road 70 65 663 Madagascar replacement cost 80 Angola 51 429 72 64 189 Namibia >R2 trillion 98 30 331 Mozambique 104 25 798 Botswana 122 15 451 Malawi 148 7 438 Lesotho 161 3 594 Swaziland 173 Mauritius 2 066 193 508 Seychelles 1 449 720 SADC Total

SOUTH AFRICA ROAD USE Freight flow on road and rail (10 th State of Logistics Survey 2014) Also important to note that of the person trips recorded in National Household Travel Survey, 2013, by transport modes are as follow: Mode Choice Factor Percentage  Minibus taxi’s (41.6%)  Private Vehicles (23.4%) Travel time 32.6  Walking (18.5%) – A long road corridors Travel Cost 26.1  busses (10.2%) Flexibility 9.2  Trains (4.4%)  Other (1.9%) Other 32.1 Roads account for 87.9% of Freight and 93.7% of Person Trips

Total Life Cycle Transportation Costs Very Good Very Poor Road User Cost is up to 90% of Total Life Cycle Transportation Cost

8 SANRAL Traffic Monitoring Stations Accurate Traffic Data – Most Important Data Item Capacity Analysis / Pavement Design / Life Cycle Economics / Toll Income

9 Traffic Monitoring Stations - WIM Current Active WIM Stations

Typical RSA WIM Station

WIM – Systematic Deviations • Main Problem - Systematic deviations in WIM observations due to quality/calibration of WIM installation. • Available Calibration Methods • On-site calibration of WIM equipment • Automatic self-calibration • Post-processing calibration • Why post-processing calibration? • Difficult to undertake full-scale on-site calibration (sample/weigh bridge) • WIM calibration tends to “drift” over time • Post-calibration method applied after load measurements. Can be reapplied to old data. • “Truck-Tractor” (TT) method - Calibration based on load observations of population sample of articulated trucks of a certain type and size • Development of method – Dr Martin Slavik/Mr Gerhard de Wet 21.8 tons

Poor WIM Installation

13 Good WIM Installation

WIM - Random Deviation • Axle load distribution • WIM Random errors and variation in dynamic loads result in: • Measured axle distribution wider than actual static load distribution • Particularly at higher end of distribution • Results in overestimation of percentage “overloaded” axles • Basic adjustment methodology • Observed axle load measurements is the sum of • Static load of the axle plus • WIM error and dynamic impact • If information on WIM error and dynamic impact is known • Then such impact can be “subtracted” from observed axle loads • To provide the static load of the axle Random Deviation Correction Important When Quantifying Overload Damage

WIM - Random Deviation • “ Expectation-Maximization-Smoothing” (EMS) algorithm • Applies a numeric technique using so-called “deconvolution” method • Wim errors basically “convolutes” or distorts the static load • Deconvolution removes this convolution from data • Central limit theorem is a special case • Numeric method does not require fitting of Log-Normal distributions • Can also be solved by means of Expectation-Maximization • Problem is that deconvolution is very sensitive to “noise” in data • Can only be used when data relatively free of noise • This problem is solved by incorporation of smoothing algorithm • Smoothing intended to remove noise from data

WIM - Random Deviation 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Obs Dist Corrected Fitted Dist Corr Fitted

17 SANRAL OVERLOAD SOFTWARE 15 to 30 % Vehicles Overloaded – Only 2% loaded beyond Prosecution Grace Statistics - Screened Sample versus Population

18 SANRAL OVERLOAD SOFTWARE

19 Committee of Transport Officials (COTO) TMH Standards

20 FUTURE WIM USE • Pavement Design/Maintenance • Old – Axle Load Histogram reduced to Equivalent Standard Axle Load per vehicle - E80 • Future – SARDS Complete Axle Load Histograms used along with Tyre Contact Stress (How load is transferred to Pavement !!!) m-shape: - Triple rectangular n-shape: - Single rectangular n-shape: - Single Circular 20 (Not to scale)

21 FUTURE WIM USE • Overload Control • Old – Screeners at Static Weigh Bridges • 50 to 100 km impact radius • Construction/Operational Costs • Human Factor • Future – WIM-Enforcement • Direct Weight Enforcement integrate with Average Speed over Distance (ASOD) – 250+ Installations • Been trialled over past 5 years • Awaiting National Regulator Compulsory Standards Type Approval for ASOD and WIM-E • End 2018 • Realtime Integration to SANRAL Central Operations Centre • Realtime Tracking of Load Movements Country Wide (OD) • Direct Enforcement (Speed/Load) • Insurance Fraud • Security Applications • Abnormal Permits Enforcement • Industry Self Regulation Verification • ??? 21 (Not to scale)

22 THANK YOU Louw Kannemeyer Engineering Executive SANRAL louwk@nra.co.za www.sanral.co.za 22